DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking

TL;DR

DeltaDock is a novel two-stage framework that improves molecular docking accuracy, efficiency, and physical reliability by combining pocket prediction as an alignment task with iterative refinement, outperforming previous GDL models.

Contribution

The paper introduces DeltaDock, a unified docking framework that reframes pocket prediction and employs a bi-level refinement process for better accuracy and physical validity.

Findings

31% success rate improvement in blind docking over previous models

Approximately 300% enhancement when considering physical validity

Superior performance demonstrated through comprehensive experiments

Abstract

Molecular docking, a technique for predicting ligand binding poses, is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods, particularly those leveraging geometric deep learning (GDL), have demonstrated significant efficiency and accuracy advantages over traditional sampling methods. Despite these advancements, current methods are often tailored for specific docking settings, and limitations such as the neglect of protein side-chain structures, difficulties in handling large binding pockets, and challenges in predicting physically valid structures exist. To accommodate various docking settings and achieve accurate, efficient, and physically reliable docking, we propose a novel two-stage docking framework, DeltaDock, consisting of pocket prediction and site-specific docking. We innovatively reframe the pocket prediction task as a pocket-ligand alignment problem rather than direct prediction in the first stage. Then we follow a bi-level coarse-to-fine iterative refinement process to perform site-specific docking. Comprehensive experiments demonstrate the superior performance of DeltaDock. Notably, in the blind docking setting, DeltaDock achieves a 31\% relative improvement over the docking success rate compared with the previous state-of-the-art GDL model. With the consideration of physical validity, this improvement increases to about 300\%.